31 research outputs found
SPIN: Simulated Poisoning and Inversion Network for Federated Learning-Based 6G Vehicular Networks
The applications concerning vehicular networks benefit from the vision of
beyond 5G and 6G technologies such as ultra-dense network topologies, low
latency, and high data rates. Vehicular networks have always faced data privacy
preservation concerns, which lead to the advent of distributed learning
techniques such as federated learning. Although federated learning has solved
data privacy preservation issues to some extent, the technique is quite
vulnerable to model inversion and model poisoning attacks. We assume that the
design of defense mechanism and attacks are two sides of the same coin.
Designing a method to reduce vulnerability requires the attack to be effective
and challenging with real-world implications. In this work, we propose
simulated poisoning and inversion network (SPIN) that leverages the
optimization approach for reconstructing data from a differential model trained
by a vehicular node and intercepted when transmitted to roadside unit (RSU). We
then train a generative adversarial network (GAN) to improve the generation of
data with each passing round and global update from the RSU, accordingly.
Evaluation results show the qualitative and quantitative effectiveness of the
proposed approach. The attack initiated by SPIN can reduce up to 22% accuracy
on publicly available datasets while just using a single attacker. We assume
that revealing the simulation of such attacks would help us find its defense
mechanism in an effective manner.Comment: 6 pages, 4 figure
AI-enabled privacy-preservation phrase with multi-keyword ranked searching for sustainable edge-cloud networks in the era of industrial IoT
Abstract: Please refer to full text to view abstrac
Towards soft real-time fault diagnosis for edge devices in industrial IoT using deep domain adaptation training strategy
Abstract: Artificial intelligence and industrial internet of things (IIoT) have been rejuvenating the fault diagnosis systems in Industry 4.0 for avoiding major financial losses caused by faults in rotating machines. Meanwhile, the diagnostic systems are provided with a number of sensory inputs that introduce variations in input space which causes difficulty for the algorithms in edge devices. This issue is generally dealt with bi-view cross-domain learning approach. We propose a soft real-time fault diagnosis system for edge devices using domain adaptation training strategy. The investigation is carried out using deep learning models that can learn representations irrespective of input dimensions. A comparative analysis is performed on a publicly available dataset to evaluate the efficacy of the proposed approach which achieved accuracy of 88.08%. The experimental results show that our method using long short-term memory network achieves the best results for the bearing fault detection in an IIoT environmental setting. © 2021 Elsevier Inc. All rights reserve
DBNS: A Distributed Blockchain-Enabled Network Slicing Framework for 5G Networks
5G technology is expected to enable many
innovative applications in different verticals. These
applications have heterogeneous performance
requirements (e.g., high data rate, low latency,
high reliability, and high availability). In order to
meet these requirements, 5G networks endorse
network flexibility through the deployment of new
emerging technologies, mainly network slicing
and mobile edge computing. This article introduces a distributed blockchain-enabled network slicing (DBNS) framework that enables service and
resource providers to dynamically lease resources
to ensure high performance for their end-to-end
services. The key component of our framework is
global service provisioning, which provides admission control for incoming service requests along
with dynamic resource assignment by means of
a blockchain-based bidding system. The goal is to
improve users’ experience with diverse services
and reduce providers’ capital and operational
expenditure
Ambient backcom in beyond 5G NOMA networks: A multi-cell resource allocation framework
The research of Non-Orthogonal Multiple Access (NOMA) is extensively used to improve the capacity of networks
beyond the fifth-generation. The recent merger of NOMA with ambient Backscatter Communication (BackCom),
though opening new possibilities for massive connectivity, poses several challenges in dense wireless networks.
One of such challenges is the performance degradation of ambient BackCom in multi-cell NOMA networks under
the effect of inter-cell interference. Driven by providing an efficient solution to the issue, this article proposes a
new resource allocation framework that uses a duality theory approach. Specifically, the sum rate of the multi-cell
network with backscatter tags and NOMA user equipments is maximized by formulating a joint optimization
problem. To find the efficient base station transmit power and backscatter reflection coefficient in each cell, the
original problem is first divided into two subproblems, and then the closed form solution is derived. A comparison
with the Orthogonal Multiple Access (OMA) ambient BackCom and pure NOMA transmission has been provided.
Simulation results of the proposed NOMA ambient BackCom indicate a significant improvement over the OMA
ambient BackCom and pure NOMA in terms of sum-rate gains
ChatGPT Needs SPADE (Sustainability, PrivAcy, Digital divide, and Ethics) Evaluation: A Review
ChatGPT is another large language model (LLM) inline but due to its
performance and ability to converse effectively, it has gained a huge
popularity amongst research as well as industrial community. Recently, many
studies have been published to show the effectiveness, efficiency, integration,
and sentiments of chatGPT and other LLMs. In contrast, this study focuses on
the important aspects that are mostly overlooked, i.e. sustainability, privacy,
digital divide, and ethics and suggests that not only chatGPT but every
subsequent entry in the category of conversational bots should undergo
Sustainability, PrivAcy, Digital divide, and Ethics (SPADE) evaluation. This
paper discusses in detail about the issues and concerns raised over chatGPT in
line with aforementioned characteristics. We support our hypothesis by some
preliminary data collection and visualizations along with hypothesized facts.
We also suggest mitigations and recommendations for each of the concerns.
Furthermore, we also suggest some policies and recommendations for AI policy
act, if designed by the governments.Comment: 15 pages, 5 figures, 4 table
Impact of Propagation Impairments on Outdoor and Indoor Optical Wireless Communications
In this contribution, we discuss the impact of propagation impairments in indoor and outdoor optical wireless communication. In outdoor terrestrial systems, fog attenuation is the major effect which limit the link length whereas in indoor systems, multipath bounds the available bandwidth and both above parameters quantified through measurements and simulations
Toward Energy-Efficient Distributed Federated Learning for 6G Networks
The provision of communication services via portable and mobile devices, such as aerial base stations, is a crucial concept to be realized in 5G/6G
networks. Conventionally, IoT/edge devices need to transmit data directly to the base station for training the model using machine learning techniques.
The data transmission introduces privacy issues that might lead to security concerns and monetary losses. Recently, federated learning was proposed to
partially solve privacy issues via model sharing with the base station. However, the centralized nature of federated learning only allows the devices within the vicinity of base stations to share trained models. Furthermore, the long-range communication compels the devices to increase transmission power, which raises energy efficiency concerns. In this work, we propose the distributed federated learning (DBFL) framework that overcomes the connectivity and energy efficiency issues for distant devices. The DBFL framework is compatible with mobile edge computing architecture that connects the devices in a distributed manner using clustering protocols. Experimental results show that the framework increases the classification performance by 7.4 percent in comparison to conventional federated learning while reducing the energy consumption